I think complexity has a lot to offer, and I will try to show that part of this potential is yet unexplored. In the previous posts, I referred to some of the uses complexity has been put to in the field of science for governance, namely, (i) using complexity to criticize the linear get-the-facts-then-act model of science for policy; (ii) using complexity to explain, name and label the policy process; (iii) using complexity to argue for the need of humility and reflexivity in the use of evidence for governance. These roles can, and have been combined, and as usual, by speaking of three distinct roles, I am simplifying and creating pure categories that do not reflect complex practice. But the point I want to make is that complexity needs to be taken further: complexity can help us re-think the science-policy interface, or as I will put it, the interface between multiple forms of knowledge and policy. Let’s dig into this question.
Blog post IV: Mobilising different types of knowledge through complexity
In order to discuss the yet unexplored contribution of complexity theory to science for governance, I introduce semiotics. Many complexity authors refer to Peirce’s semiotics (1935), including Allen & Starr (1982); Kovacic & Giampietro (2015); Salthe (1993) – and Kohn, (2013) in his great book How forests think.
In a nutshell, semiotics is the study of meaning generated through the interpretation of signs (Salthe 1993). The novelty introduced by Peirce is the focus on signs. If one were to think of science, in simplified terms, as the study of things in the world, then semiotics would be an argument that understands “things” and “the world” as mutually constituted through a hermeneutic process. This hermeneutic process depends on signs and their interpretation. According to Peirce, there are three elements that constitute the semiotic process: the sign, the object and the interpretant. The interpretant, or observer, is generally taken out of science. Salthe argues that “Positivist models after Comte never included the observer explicitly … the observer could largely be taken for granted (since all science was done by a single class).” So science is reduced to what it studies – if celestial bodies, then it is physics, if living beings, then it is biology, if people, then psychology, anthropology, economics. The fact that the observer matters has been an argument, among others, of feminist theory. In addition, Peirce points to the fact that the sign matters.
To use Kohn’s example, walking sticks base their survival strategy on their iconicity to twigs. How predators see (or fail to see) them matters vitally (Kohn, 2013). The quality of the representation (similarity to twigs) is given by the semiotic process. If the similarity is correctly interpreted by predators (interpretant), walking sticks will not to be eaten. The sign isn’t “true” because it represents the essence of twigs, but because it is interpreted correctly by predators. Semiotic processes are highly contextual, as the iconicity of walking sticks depends on them being in a forest. Walking sticks and the forest (the object and its world) are mutually constituted, and walking sticks interact with the world through a sign process. The survival and reproduction of living systems is based on a successful series of semiotic processes. Signs are validated through several iterations of the semiotic process, which can be associated with evolution.
Pierce’s triadic relation can also be described using verbs: represent, transduce and apply. A semiotic process is one in which the interpretation of signs is validated through experience. This relational understanding of signs, objects and their application is also found in Rosen (1985, 1991). Rosen refers to models (signs), observed systems (objects) and anticipation as a form of application. In both Peirce and Rosen, experience ensures the quality of the representation.
Semiotics shows that the knowledge needed for governance must be validated by experience. This insight has profound implications for the use of science for governance. It means that laboratory science may not be fit for policy advice as is. This does not mean that laboratory science is less valid, it just means that it is not fit for purposes other than what it was created for: for instance, probing the presence of a chemical in mice. The focus on experience shifts attention from truth to quality, which Funtowicz and Ravetz define as “fitness for purpose” (Funtowicz & Ravetz, 1992). Quality is validated through experience. How good the similarity of walking sticks is to twigs is given by the success in surviving (fitness for purpose), not by knowledge about the nature of twigs.
Scott (1998) gives numerous examples of how knowledge abstracted from experience goes wrong when applied to governance (his book Seeing like a state is a must-read!). He uses the case of forest management, which is based on a reductionist understanding of a forest as trees that can be planted, ignoring the undergrowth, the animals, the different ages, size and types of trees. Scientific forestry freezes a living process into measurable variables that can be seen and managed at a distance. Scientific forestry also fails to reproduce a forest, and has led to soil erosion, biodiversity loss and poor growth of planted trees in the long term. Another example is that of planned cities, in which different areas are planned for different functions – such as work, residence, leisure. Also in this case, the life of the city is lost, as there will not be life on the streets once the tasks performed in that area are completed, which decreases safety, neighbourly life and the sense of belonging to a place. A powerful metaphor that Scott uses throughout the book is that of taxidermy: scientific knowledge does reproduce forests, cities, villages, agricultural practices, but as lifeless representations of the material part of a forest, a city, a village.
What type of signs does modern science produce? Modern science, abstracted from the social context in which it is produced (because it needs to be objective) and from the context of application (because it needs to be reproducible and universal) produces signs that may be fit for description, but are not fit for guiding the governance of dynamic, complex and living societies, forests, and ecosystems.
Scott concludes his analysis by referring to Peirce and arguing for the value of metis, as opposed to techne. Metis is “a wide array of practical skills and acquired intelligence in responding to a constantly changing natural and human environment” (p. 313). That is, knowledge for governance emerges after signs are validated by experience. What is needed to guide governance is not just pluralism in scientific disciplines, but pluralism in the types of knowledge that are used, in order to include practical knowledge (Scott, 1998), experience (Allen & Starr, 1982), craftmanship and tacit knowledge (Funtowicz & Ravetz, 1990). Taleb (2007, 2018) talks about having skin in the game. Skin in the game means that one worries not only about giving advice, but also about the result of the application. Skin in the game positions science within the semiotic process.
The challenge is that metis cannot be taught in classrooms or through books. Technical knowledge can be produced through research practice. Practical knowledge requires experience, takes time. Practical knowledge is acquired by practice and repetition.
An important insight of complexity is that the processes of constructing a model (sign), narrating (using the model to guide action), becoming (learning from experience), and observing (updating the semiotic process as a whole), work at different rates and correspond to different time scales (Allen & Starr, 1982). The challenge is not only to combine different types of knowledge, which cannot be codified and transmitted in the same way, but also that this multi-speeds process cannot be planned. With this note of caution, I argue that complexity theory makes the case for the need to integrate multiple types of knowledge in governance, and gives some insights about the processes through which these different types of knowledge are generated and interact.
References
Allen, T. F. H., & Starr, T. B. (1982). Hierarchy perspectives for ecological complexity. Chicago: University of Chicago Press.
Funtowicz, S. O., & Ravetz, J. R. (1990). Uncertainty and quality in science for policy. Ecological Economics (Vol. 6). Dorcrecht: Kluwer Academic Publishers.
Funtowicz, S. O., & Ravetz, J. R. (1992). The good, the true and the post-modern. Futures, 24(December), 963–976.
Kohn, E. (2013). How forests think. Berkley: University of California Press.
Kovacic, Z., & Giampietro, M. (2015). Beyond “beyond GDP indicators”: the need for reflexivity in science for governance. Ecological Complexity, 21, 53–61.
Peirce, C. S. (1935). Collected papers. Vol. VI. (C. Hartshorne & P. Weiss, Eds.). Boston: Harvard University Press.
Rosen, R. (1985). Anticipatory systems. Oxford: Pergamon Press.
Rosen, R. (1991). Life itself: A comprehensive inquiry into the nature, origin, and fabrication of life. New York: Columbia University Press.
Salthe, S. N. (1993). Development and evolution: Complexity and change in biology. Boston: MIT Press.
Scott, J. (1998). Seeing like a state: How certain scheme to improve the human condition have failed. New Haven: Yale University Press.
Taleb, N. N. (2007). The black swan. New York: Random House.
Taleb, N. N. (2018). Skin in the game: Hidden asymmetries in daily life. New York: Random House.
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